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The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes.
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ShapeNet: An Information-Rich 3d Model Repository
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Fully convolutional networks for semantic segmentation
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U-net: Convolutional networks for biomedical image segmentation
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Semanticpaint: Interactive 3d labeling and learning at your fingertips
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Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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M. Noroozi and P. Favaro · 2016
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C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. Guibas · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Colorful image colorization
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Good semi-supervised learning that requires a bad gan
Z. Dai, Z. Yang, F. Yang, W. W. Cohen, and R. R. Salakhutdinov · 2017
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Adversarial feature learning
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Learning to generate chairs, tables and cars with convolutional networks
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Hypernetworks
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Learning a multi-view stereo machine
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Colorization as a proxy task for visual understanding
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2017
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Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
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Interpretable transformations with encoder-decoder networks
D. E. Worrall, S. J. Garbin, D. Turmukhambetov, and G. J. Brostow · 2017
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Learning representations and generative models for 3D point clouds
Data-efficient image recognition with contrastive predictive coding
O. J. Hénaff, A. Razavi, C. Doersch, S. Eslami, and A. v. d. Oord · 2019
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Neural volumes: Learning dynamic renderable volumes from images
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Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Implicit surface representations as layers in neural networks
M. Michalkiewicz, J. K. Pontes, D. Jack, M. Baktashmotlagh, and A. Eriksson · 2019
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PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su · 2019
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas · 2018
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3dmv: Joint 3d-multi-view prediction for 3d semantic scene segmentation
A. Dai and M. Nießner · 2018
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Neural scene representation and rendering
S. A. Eslami, D. J. Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, et al · 2018
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Atlasnet: A papier-mâché approach to learning 3d surface generation
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry · 2018
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Learning free-form deformations for 3d object reconstruction
D. Jack, J. K. Pontes, S. Sridharan, C. Fookes, S. Shirazi, F. Maire, and A. Eriksson · 2018
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Learning category-specific mesh reconstruction from image collections
A. Kanazawa, S. Tulsiani, A. A. Efros, and J. Malik · 2018
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Neural 3d mesh renderer
H. Kato, Y. Ushiku, and T. Harada · 2018
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Hologan: Unsupervised learning of 3d representations from natural images
T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y. Yang · 2019
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Occupancy flow: 4d reconstruction by learning particle dynamics
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2019
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Texture fields: Learning texture representations in function space
M. Oechsle, L. Mescheder, M. Niemeyer, T. Strauss, and A. Geiger · 2019
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DeepSDF: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
S. Saito, Z. Huang, R. Natsume, S. Morishima, A. Kanazawa, and H. Li · 2019
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DeepVoxels: Learning persistent 3D feature embeddings
V. Sitzmann, J. Thies, F. Heide, M. Nießner, G. Wetzstein, and M. Zollhöfer · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
V. Sitzmann, M. Zollhöfer, and G. Wetzstein · 2019
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Pushing the boundaries of view extrapolation with multiplane images
P. P. Srinivasan, R. Tucker, J. T. Barron, R. Ramamoorthi, R. Ng, and N. Snavely · 2019
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Deferred neural rendering: Image synthesis using neural textures
J. Thies, M. Zollhöfer, and M. Nießner · 2019
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Learning spatial common sense with geometry-aware recurrent networks
H.-Y. F. Tung, R. Cheng, and K. Fragkiadaki · 2019
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Gspn: Generative shape proposal network for 3d instance segmentation in point cloud
L. Yi, W. Zhao, H. Wang, M. Sung, and L. Guibas · 2019
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Sal: Sign agnostic learning of shapes from raw data
M. Atzmon and Y. Lipman · 2020
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Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
R. Chabra, J. E. Lenssen, E. Ilg, T. Schmidt, J. Straub, S. Lovegrove, and R. Newcombe · 2020
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Cvxnet: Learnable convex decomposition
B. Deng, K. Genova, S. Yazdani, S. Bouaziz, G. Hinton, and A. Tagliasacchi · 2020
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Local deep implicit functions for 3d shape
K. Genova, F. Cole, A. Sud, A. Sarna, and T. Funkhouser · 2020
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Implicit geometric regularization for learning shapes
A. Gropp, L. Yariv, N. Haim, M. Atzmon, and Y. Lipman · 2020
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Local implicit grid representations for 3d scenes
C. Jiang, A. Sud, A. Makadia, J. Huang, M. Nießner, and T. Funkhouser · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
L. Jing and Y. Tian · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2020
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Convolutional occupancy networks
S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys, and A. Geiger · 2020
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Metasdf: Meta-learning signed distance functions
V. Sitzmann, E. R. Chan, R. Tucker, N. Snavely, and G. Wetzstein · 2020
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Implicit neural representations with periodic activation functions
V. Sitzmann, J. N. Martel, A. W. Bergman, D. B. Lindell, and G. Wetzstein · 2020
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State of the art on neural rendering
A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, K. Sunkavalli, R. Martin-Brualla, T. Simon, J. Saragih, M. Nießner, et al · 2020
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